Presently, there is a vast amount of video media available to every person. From films or movies to broadcast television programs to cable or satellite television programs to home movies or user-created video clips, there are many repositories and databases from which people may choose and obtain video content in various formats, and the amount of video content available continues to grow at a very high rate. Broadcast, cable, or satellite companies often provide hundreds of different channels for viewers to choose from. Movie rental companies such as Netflix and Blockbuster have tens, even hundreds, of thousands of titles on DVDs (digital video disc) or video cassettes. More recently, the Internet has also lent its unique capability and become a great repository and distribution channel for video media world-wide. Websites such as YouTube and AOL Video have immense video collections, often millions of video clips, contributed by users from all over the world.
Faced with such a great and overwhelming number of choices, people often have a difficult time finding the specific video content they are looking for. For example, suppose a person is looking for videos related to the subject matter of figure skating at YouTube's website, and conducts a keyword search using the term “figure skating.” A recent search of this type would present the person with over fifty-six thousand video clips, all presumably relating to figure skating, and the amount of content that is available is continually growing. It is very unlikely that the person will watch all fifty-six thousand video clips to find those few that he or she likes. Using more specific search terms helps narrow down the search results. For example, conducting a search using the term “figure staking Olympic championship” instead of just “figure skating” at YouTube's website will result in over one hundred related video clips, a much smaller number compare to the first search result, and yet still a lot of video clips for the person to go through. In addition, using more specific search terms sometimes results in certain video clips being filtered out unintentionally or mistakenly, perhaps due to these video clips having incorrect, imprecise, or incomplete content descriptions. On the other hand, sometimes people are not certain what terms or keywords to search for. For example, suppose a person is looking for a movie to rent at Netflix's website. The person wants to rent a comedy-type movie, but does not have any specific titles in mind. In this case, it is difficult for the person to search for a specific movie using a few keywords. Instead, the person is more likely to browse through the comedy category and read about the descriptions of individual movies in order to decide whether he or she likes that particular movie. Since there are hundreds or thousands of movies available in each category, it often takes a long time to find a particular movie to the person's liking.
When presented with too many choices, people often give up after going through the first few choices on the list, perhaps due to lack of time or loss of patience or interest. As a result, the majority of the available video content are known only to a very few people, while most people are aware of only a small number of selected or dominant videos. This scenario may be represented using a polynomial curve 100 as shown in FIG. 1, where the x-axis represents the number of available video content and the y-axis represents the number of times individual pieces of video content have been viewed. To the left of the curve 100, a small number of the available video content are viewed many times, and they are the dominant video content. To the right, the majority of the video content are viewed only a few times, which is sometimes referred to as the “long tail” of the curve 100.
For most people, there is video content in the long tail portion of the curve 100 that they will want to view, and yet, they are not aware of the existence of these videos. To help people locate video content that they are unaware of and yet may enjoy, websites often make recommendations to their customers or users based on various criteria. For example, when a person rents a movie from Netflix, Netflix recommends other movies selected from the same genre or related subject matter or by the same actors and/or actresses. When a person buys a DVD from Amazon, Amazon recommends other movies bought by those customers who also have bought this DVD. To a certain extent, such websites strive to provide personalized services toward individual customers.
On the other hand, users of consumer electronic devices often are not afforded as many personalized choices as provided by the websites. For example, suppose a person wishes to watch television. He or she may only choose from a fixed number of available programs aired on a fixed number of channels at any given time. The person generally is not able to decide what program is aired on what channel at what time. Such decisions usually rest with the television stations. Thus, if the person wishes to watch a program that is not aired on any of the available channels, he or she will have to forego the desire or find alternative options.